Velocity Obstacle Based Conflict Avoidance in Urban Environment with Variable Speed Limit

Current investigations into urban aerial mobility, as well as the continuing growth of global air transportation, have renewed interest in conflict detection and resolution (CD&R) methods. The use of drones for applications such as package delivery, would result in traffic densities that are orders of magnitude higher than those currently observed in manned aviation. Such densities do not only make automated conflict detection and resolution a necessity, but will also force a re-evaluation of aspects such as coordination vs. priority, or state vs. intent. This paper looks into enabling a safe introduction of drones into urban airspace by setting travelling rules in the operating airspace which benefit tactical conflict resolution. First, conflicts resulting from changes of direction are added to conflict resolution with intent trajectory propagation. Second, the likelihood of aircraft with opposing headings meeting in conflict is reduced by separating traffic into different layers per heading–altitude rules. Guidelines are set in place to make sure aircraft respect the heading ranges allowed at every crossed layer. Finally, we use a reinforcement learning agent to implement variable speed limits towards creating a more homogeneous traffic situation between cruising and climbing/descending aircraft. The effects of all of these variables were tested through fast-time simulations on an open source airspace simulation platform. Results showed that we were able to improve the operational safety of several scenarios.

[1]  Kagan Tumer,et al.  A multiagent approach to managing air traffic flow , 2010, Autonomous Agents and Multi-Agent Systems.

[2]  John-Paul Clarke,et al.  A mixed integer program for flight-level assignment and speed control for conflict resolution , 2009, Proceedings of the 48h IEEE Conference on Decision and Control (CDC) held jointly with 2009 28th Chinese Control Conference.

[3]  Juan A. Besada,et al.  Drone Flight Planning for Safe Urban Operations: UTM Requirements and Tools , 2019, 2019 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops).

[4]  John Lygeros,et al.  A simulation based study of subliminal control for air traffic management , 2010 .

[5]  Max Mulder,et al.  Impact of aircraft speed heterogeneity on contingent flow control in 4D en-route operation , 2020 .

[6]  Weiyi Liu,et al.  Probabilistic Trajectory Prediction and Conflict Detection for Air Traffic Control , 2011 .

[7]  Jongho Park,et al.  Collision Avoidance of Hexacopter UAV Based on LiDAR Data in Dynamic Environment , 2020, Remote. Sens..

[8]  Sheng Li,et al.  Optimizing Collision Avoidance in Dense Airspace using Deep Reinforcement Learning , 2019, ArXiv.

[9]  Max Mulder,et al.  Solution-Space-Based Analysis of Dynamic Air Traffic Controller Workload , 2015 .

[10]  A Deep Multi-Agent Reinforcement Learning Approach to Autonomous Separation Assurance , 2020, ArXiv.

[11]  Philip Bachman,et al.  Deep Reinforcement Learning that Matters , 2017, AAAI.

[12]  Eric Haines,et al.  Point in Polygon Strategies , 1994, Graphics Gems.

[13]  G. Gawinowski,et al.  ERASMUS: a new path for 4d trajectory-based enablers to reduce the traffic complexity , 2007, 2007 IEEE/AIAA 26th Digital Avionics Systems Conference.

[14]  Wei Wang,et al.  Reinforcement Learning-Based Variable Speed Limit Control Strategy to Reduce Traffic Congestion at Freeway Recurrent Bottlenecks , 2017, IEEE Transactions on Intelligent Transportation Systems.

[15]  Paolo Fiorini,et al.  Motion Planning in Dynamic Environments Using Velocity Obstacles , 1998, Int. J. Robotics Res..

[16]  Debasish Ghose,et al.  Obstacle avoidance in a dynamic environment: a collision cone approach , 1998, IEEE Trans. Syst. Man Cybern. Part A.

[17]  René van Paassen,et al.  The Use of Intent Information in Conflict Detection and Resolution Models Based on Dynamic Velocity Obstacles , 2015, IEEE Transactions on Intelligent Transportation Systems.

[18]  Fang Li,et al.  The Obstacle Detection Method of UAV Based on 2D Lidar , 2019, IEEE Access.

[19]  G. Uhlenbeck,et al.  On the Theory of the Brownian Motion , 1930 .

[20]  Pan Zhao,et al.  Intent-Estimation- and Motion-Model-Based Collision Avoidance Method for Autonomous Vehicles in Urban Environments , 2017 .

[21]  Pascal Bouvry,et al.  Internet of Unmanned Aerial Vehicles—A Multilayer Low-Altitude Airspace Model for Distributed UAV Traffic Management , 2019, Sensors.

[22]  Guillaume J. Laurent,et al.  Independent reinforcement learners in cooperative Markov games: a survey regarding coordination problems , 2012, The Knowledge Engineering Review.

[23]  Leonardo L. B. V. Cruciol,et al.  Reward functions for learning to control in air traffic flow management , 2013 .

[24]  Inseok Hwang,et al.  Intent-Based Probabilistic Conflict Detection for the Next Generation Air Transportation System , 2008, Proc. IEEE.

[25]  Kapil Sheth,et al.  Performance evaluation of airborne separation assurance for free flight , 2000 .

[26]  D. Alejo,et al.  Multi-UAV collision avoidance with separation assurance under uncertainties , 2009, 2009 IEEE International Conference on Mechatronics.

[27]  W. Ochieng,et al.  Strategic Conflict Detection and Resolution Using Aircraft Intent Information , 2010 .

[28]  Max Mulder.  The use of intent information in an airborne self-separation assistance display design , 2009 .

[29]  Robert L. Bertini,et al.  Traffic Management Effects of Variable Speed Limit System on a German Autobahn , 2013 .

[30]  Peter Henderson,et al.  Reproducibility of Benchmarked Deep Reinforcement Learning Tasks for Continuous Control , 2017, ArXiv.

[31]  Joost Ellerbroek,et al.  Review of Conflict Resolution Methods for Manned and Unmanned Aviation , 2020, Aerospace.

[32]  T. Rakha,et al.  Review of Unmanned Aerial System (UAS) applications in the built environment: Towards automated building inspection procedures using drones , 2018, Automation in Construction.

[33]  Victor L. Knoop,et al.  Constrained Urban Airspace Design for Large-Scale Drone-Based Delivery Traffic , 2021, Aerospace.

[34]  Matthijs T. J. Spaan,et al.  Traffic flow optimization: A reinforcement learning approach , 2016, Eng. Appl. Artif. Intell..

[35]  Lee C. Yang,et al.  USING INTENT INFORMATION IN PROBABILISTIC CONFLICT ANALYSIS , 1998 .

[36]  Pieter Abbeel,et al.  Benchmarking Deep Reinforcement Learning for Continuous Control , 2016, ICML.

[37]  Yu Liu,et al.  Intent Based Trajectory Prediction by Multiple Model Prediction and Smoothing , 2015 .

[38]  Geoff Boeing,et al.  OSMnx: New Methods for Acquiring, Constructing, Analyzing, and Visualizing Complex Street Networks , 2016, Comput. Environ. Urban Syst..

[39]  Richard Irvine.  THE GEARS CONFLICT RESOLUTION ALGORITHM , 1998 .

[40]  M. Papageorgiou,et al.  Effects of Variable Speed Limits on Motorway Traffic Flow , 2008 .

[41]  Bin Ran,et al.  Differential variable speed limits control for freeway recurrent bottlenecks via deep actor-critic algorithm , 2020 .